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[Notebook] Create Notebook_en #5214

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Update PP-OCRv2预测部署实战.ipynb
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8 changes: 4 additions & 4 deletions notebook/notebook_ch/2.text_detection/文本检测FAQ.ipynb
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"\n",
"如果对某些层使用更小的学习率学习，静态图里还不是很方便，一个方法是在参数初始化的时候，给权重的属性设置固定的学习率，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr\n",
"\n",
"实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错\n",
"实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错。\n",
"\n",
"**1.11 DB的预处理部分，图片的长和宽为什么要处理成32的倍数？**\n",
"\n",
Expand All @@ -95,7 +95,7 @@
"\n",
"**1.13 PP-OCR检测效果不好，该如何优化？**\n",
"\n",
"A： 具体问题具体分析:\n",
"**A**： 具体问题具体分析:\n",
"- 如果在你的场景上检测效果不可用，首选是在你的数据上做finetune训练；\n",
"- 如果图像过大，文字过于密集，建议不要过度压缩图像，可以尝试修改检测预处理的resize逻辑，防止图像被过度压缩；\n",
"- 检测框大小过于紧贴文字或检测框过大，可以调整db_unclip_ratio这个参数，加大参数可以扩大检测框，减小参数可以减小检测框大小；\n",
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"\n",
"**A**：GPU加速预测推荐使用TensorRT。\n",
"- 1. 从[链接](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html)下载带TensorRT的Paddle安装包或者预测库。\n",
"- 2. 从Nvidia官网下载TensorRT版本，注意下载的TensorRT版本与paddle安装包中编译的TensorRT版本一致。\n",
"- 3. 设置环境变量LD_LIBRARY_PATH，指向TensorRT的lib文件夹\n",
"- 2. 从Nvidia官网下载[TensorRT](https://developer.nvidia.com/tensorrt)，注意下载的TensorRT版本与paddle安装包中编译的TensorRT版本一致。\n",
"- 3. 设置环境变量`LD_LIBRARY_PATH`，指向TensorRT的lib文件夹\n",
"```\n",
"export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>\n",
"```\n",
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52 changes: 26 additions & 26 deletions notebook/notebook_ch/2.text_detection/文本检测实践篇.ipynb
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16 changes: 8 additions & 8 deletions notebook/notebook_ch/2.text_detection/文本检测理论篇.ipynb
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"collapsed": false
},
"source": [
"# OCR七日课之文本检测综述\n"
"# 文本检测算法理论\n"
]
},
{
Expand All @@ -15,11 +15,11 @@
"collapsed": false
},
"source": [
"## 1. 文本检测\n",
"## 1 文本检测\n",
"\n",
"文本检测任务是找出图像或视频中的文字位置。不同于目标检测任务，目标检测不仅要解决定位问题，还要解决目标分类问题。\n",
"\n",
"文本在图像中的表现形式可以视为一种‘目标‘，通用的目标检测的方法也适用于文本检测，从任务本身上来看：\n",
"文本在图像中的表现形式可以视为一种‘目标’，通用的目标检测的方法也适用于文本检测，从任务本身上来看：\n",
"\n",
"- 目标检测：给定图像或者视频，找出目标的位置（box），并给出目标的类别；\n",
"- 文本检测：给定输入图像或者视频，找出文本的区域，可以是单字符位置或者整个文本行位置；\n",
Expand All @@ -41,14 +41,14 @@
"1. 自然场景中文本具有多样性：文本检测受到文字颜色、大小、字体、形状、方向、语言、以及文本长度的影响；\n",
"2. 复杂的背景和干扰；文本检测受到图像失真，模糊，低分辨率，阴影，亮度等因素的影响；\n",
"3. 文本密集甚至重叠会影响文字的检测；\n",
"4. 文字存在局部一致性，文本行的一小部分，也可视为是独立的文本；\n",
"4. 文字存在局部一致性：文本行的一小部分，也可视为是独立的文本。\n",
"\n",
"<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/072f208f2aff47e886cf2cf1378e23c648356686cf1349c799b42f662d8ced00\"\n",
"width=\"1000\" ></center>\n",
"\n",
"<br><center>图3 文本检测场景</center>\n",
"\n",
"针对以上问题，衍生了很多基于深度学习的文本检测算法，解决自然场景文字检测问题，这些方法可以分为基于回归和基于分割的文本检测方法。\n",
"针对以上问题，衍生出了很多基于深度学习的文本检测算法，用于解决自然场景文字检测问题。这些方法可以分为基于回归和基于分割的文本检测方法。\n",
"\n",
"下一节将简要介绍基于深度学习技术的经典文字检测算法。"
]
Expand All @@ -59,7 +59,7 @@
"collapsed": false
},
"source": [
"## 2. 文本检测方法介绍\n",
"## 2 文本检测方法介绍\n",
"\n",
"\n",
"近些年来基于深度学习的文本检测算法层出不穷，这些方法大致可以分为两类：\n",
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"\n",
"\n",
"\n",
"LOMO[19]针对长文本和弯曲文本问题，提出迭代的优化文本定位特征获取更精细的文本定位，该方法包括三个部分，坐标回归模块DR，迭代优化模块IRM以及任意形状表达模块SEM。分别用于生成文本大致区域，迭代优化文本定位特征，预测文本区域、文本中心线以及文本边界。迭代的优化文本特征可以更好的解决长文本定位问题以及获得更精确的文本区域定位。\n",
"LOMO[19]针对长文本和弯曲文本问题，提出迭代的优化文本定位特征获取更精细的文本定位。该方法包括三个部分：坐标回归模块DR，迭代优化模块IRM以及任意形状表达模块SEM。它们分别用于生成文本大致区域，迭代优化文本定位特征，预测文本区域、文本中心线以及文本边界。迭代的优化文本特征可以更好的解决长文本定位问题以及获得更精确的文本区域定位。\n",
"<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/e90adf3ca25a45a0af0b84a181fbe2c4954be1fcca8f4049957128548b7131ef\"\n",
"width=\"1000\" ></center>\n",
"<br><center>图11 LOMO框架图</center>\n",
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"collapsed": false
},
"source": [
"## 3. 总结\n",
"## 3 总结\n",
"\n",
"本节介绍了近几年来文本检测领域的发展，包括基于回归、分割的文本检测方法，并分别列举并介绍了一些经典论文的方法思路。下一节以PaddleOCR开源库为例，详细介绍DBNet的算法原理以及核心代码实现。"
]
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33 changes: 17 additions & 16 deletions notebook/notebook_ch/3.text_recognition/文本识别实践部分.ipynb
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30 changes: 15 additions & 15 deletions notebook/notebook_ch/5.ppocrv2_inference_deployment/PP-OCRv2预测部署实战.ipynb
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2 changes: 1 addition & 1 deletion notebook/notebook_ch/如何使用本书.ipynb
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"\n",
"然后安装第三方库：\n",
"\n",
"```\n",
"```bash\n",
"cd PaddleOCR\n",
"pip3 install -r requirements.txt\n",
"```\n",
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